Machine learning-assisted network pharmacology reveals that the Chaihu-Longgu-Muli decoction modulates the inflammatory microenvironment to treat perimenopausal syndrome.

Background: Chaihu-Longgu-Muli decoction (CLMD) is a traditional Chinese medicine formula that shows promise in alleviating symptoms related to premenstrual syndrome (PMS). However, the underlying mechanism remains unclear. This study uses a machine learning-assisted framework integrated with network pharmacology and experimental validation to elucidate the key targets and signaling pathways involved in the therapeutic effects of CLMD on PMS. Methods: We developed an integrative research framework that incorporates network pharmacology, machine learning, molecular dynamics, and Results: By constructing an intersecting network of CLMD-active ingredient-disease targets, we identified 204 representative active components and nearly 300 potential targets. Intersecting these genes with PMS-related genes yielded 46 key targets. The PPI network built in Cytoscape/STRING, together with multiple machine learning algorithms (LASSO, SVM-RFE, and random forest), was used to select key targets, including IL6, TNF, and IL1B. At the molecular level, the key active components (quercetin, kaempferol, and wogonin) showed strong docking affinities to these targets (binding energies <-5.0 kcal/mol) and exhibited stable MD conformations. CLMD intervention significantly downregulated IL6, TNF, and IL1B, reduced reactive oxygen species (ROS) accumulation, and promoted macrophage polarization from the proinflammatory M1 phenotype to the reparative M2 phenotype. Consequently, the experimental findings corroborate the network pharmacology predictions. Conclusion: CLMD exerts its therapeutic effects through multicomponent-multitarget-multipathway synergy that modulates the inflammatory microenvironment, which provides mechanistic insight into relieving the multidimensional symptoms of PMS.